Uncertainty analysis of step-selection functions: The effect of model parameters on inferences about the relationship between animal movement and the environment
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Date
2014-09
Authors
Holloway, Paul
Miller, Jennifer A.
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Springer
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Abstract
As spatio-temporal movement data is becoming more widely available for analysis in GIS and related areas, new methods to analyze them have been developed. A step-selection function (SSF) is a recently developed method used to quantify the effect of environmental factors on animal movement. This method is gaining traction as an important conservation tool; however there have been no studies that have investigated the uncertainty associated with subjective model decisions. In this research we used two types of animals – oilbirds and hyenas – to examine how systematically altering user decisions of model parameters influences the main outcome of an SSF, the coefficients that quantify the movement-environment relationship. We found that user decisions strongly influence the results of step-selection functions and any subsequent inferences about animal movement and environmental interactions. Differences were found between categories for every variable used in the analysis and the results presented here can help to clarify the sources of uncertainty in SSF model decisions.
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Keywords
Step-selection functions , Uncertainty analysis , Animal movement , Conditional logistic regression
Citation
Holloway, P. and Miller, J. A. (2014) 'Uncertainty Analysis of Step-Selection Functions: The Effect of Model Parameters on Inferences about the Relationship between Animal Movement and the Environment', GioScience 2014, Lecture Notes in Computer Science, LCNS 8728, pp. 48-63. doi: 10.1007/978-3-319-11593-1_4
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© Springer International Publishing Switzerland 2014. This is a post-peer-review, pre-copyedit version of an article published in Lecture Notes in Computer Science. The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-319-11593-1_4